A hyper-sphere SVM introduced the margin

Binary hyper-sphere support vector machine (SVM) is a new method for data description. Its weakness is that the margin between two classes of samples is zero or an uncertain value, which affects the classifier´s generalization performance to some extent. So a generalized hyper-sphere SVM (GHSSVM) is provided in this paper. By introducing the parameter n and b (n>b), the margin which is greater than zero may be obtained. The experimental results show the proposed classifier may have better generalization performance and the less experimental risk than the hyper-sphere SVM in the references.